Estimating Potential Evapotranspiration in Maranhão State Using Artificial Neural Networks

Klara Cunha De Meneses,Kamila Cunha De Meneses,Maryzélia Furtado De Farias,Lucas Eduardo De Oliveira Aparecido

doi:10.1590/0102-77863540072

Abstract

Abstract The use of technology and planning in agricultural production is essential in Northeastern Brazil, which is the region of the country that most suffers from water shortage. For the best irrigation management, it is necessary to know the potential evapotranspiration rate for water control in order to increase productivity. There are several direct and indirect methods for estimating evapotranspiration, but the standard method recommended by the United Nations Agriculture Organization (FAO) is the Penman-Monteith (PETpm) method because it has higher accuracy than other methods. However, it is a difficult method to be used due to the need for a large number of meteorological elements. In this context, the objective of this study was to estimate potential evapotranspiration by the Penman-Monteith method in the micro-region of Baixo Parnaíba in Maranhão state using artificial neural networks. Agro-meteorological data were collected daily over 34 years, from 1984 to 2017, and these data were obtained from the NASA/POWER website. Subsequently, liquid radiation and potential evapotranspiration were calculated by the Penman-Monteith standard method (1998). To predict potential daily evapotranspiration, the Multi-Layer Perceptron (MLP) was chosen, which is a traditional Artificial Neural Network. The period that presented a higher evapotranspiration index was the same one that showed precipitation with a lower volume and higher temperatures. The artificial neural network model that best adapted to estimate PETpm was MLP 2-5-1. It is concluded that artificial neural networks estimate with accuracy and precision the Penman-Monteith daily potential evapotranspiration of the Lower Parnaiba in Maranhão, and potential evapotranspiration can be estimated by the Penman-Monteith method using neural networks with inputs of air temperatures.

Highlights

Water shortage in the Northeast region of Brazil is a great problem compared to other regions of the country, and for the expansion of the agricultural sector and the rational use of water it is essential to apply technology and planning for the most efficient irrigation to occur in this region (Andrade Junior et al, 2018; Freitas et al, 2018)
The study of daily evapotranspiration rates is very useful for establishing water requirements in agriculture (Martins and Rosa, 2019)
ANN applications have been used in the estimation of PET and the results suggest that artificial neural networks are more accurate than conventional methods (Dai et al, 2009; Abdullahi et al, 2017)

Summary

Introduction

Water shortage in the Northeast region of Brazil is a great problem compared to other regions of the country, and for the expansion of the agricultural sector and the rational use of water it is essential to apply technology and planning for the most efficient irrigation to occur in this region (Andrade Junior et al, 2018; Freitas et al, 2018). Establishing the water requirements of a crop in its different stages of development is of fundamental importance for irrigation management to acheive good productivity and rational water use (Costa et al, 2018). For this to occur it is necessary to know the evapotranspiration rate of the plant in order to irrigate with the correct amount, with the goal of increasing production without waste of water. Evapotranspiration is the process in which water from the earth's surface evaporates into the atmosphere together with the transpiration of plants, being an important part of the hydrological cycle (Alves et al, 2017). The study of daily evapotranspiration rates is very useful for establishing water requirements in agriculture (Martins and Rosa, 2019)

Objectives

Methods

Results